Abstract: To optimize the quality of milling process, a milling parameter optimization method based on hesitant fuzzy decision making is studied. According to the mechanism of milling cutting process, the controllable factors are introduced into the experiment firstly. The mathematical optimization model of milling parameter is established based on experimental data. Then it combines the hesitant Euclidean distance with fuzzy logic inference to simplify the multi-response system in the milling process. The above process avoids the setting of right weights in the traditional fuzzy measure method and extracts the effective information of the correlation of the response at the same time. Finally, the most suitable combination of parameters is obtained through the main effect analysis among the controllable factors and the output value of the fuzzy inference process:when the feed speed is 0.01 mm/tooth, the cutting depth is 0.064 mm, the cutting speed reaches 396 m/min, and the cutting width reaches 12.26 mm, the surface roughness R_a and R_t of the machined components are optimized, which improves the quality of the machining parts. From the result, it is clear that the method of hesitant fuzzy decision theory is applied to the optimization of milling parameters for the first time. This application avoids the information loss caused by the mean processing method and can increase the robustness of experimental design. Compared with the desirability function method, the proposed milling parameter optimization method based on hesitant fuzzy decision is not restricted by the right weight and it can optimize the two responses of the process at the same time, which has practical effectiveness and reliability.

Key words: CNC milling process, hesitation Euclidean distance, fuzzy logic inference, multi-response parameter optimization